Predictive Analytics with TensorFlow 6.5: Using Word2vec for Sentiment Analysis

The video tutorial covers sentiment analysis using various models, including TF-IDF and Sibo. It explains the process of building a predictive model for movie review sentiment analysis using the Sibo method, with data from the Cornell University dataset. The tutorial details data preprocessing, dictionary building, and the creation and training of the Sibo model using TensorFlow. It also demonstrates reusing the trained model for sentiment prediction and concludes with a brief introduction to deep neural networks.

What is the primary focus of the initial section of the video?

• Explaining the TF-IDF model

• Introducing the CBoW model for word embedding

• Discussing the architecture of neural networks

• Describing the Cornell University movie review dataset

What is the first step in preparing the movie review dataset?

• Splitting the data into training and test sets

• Building a dictionary of words

• Normalizing the text

• Downloading and preprocessing the data

Which function is used to convert text data into numerical data?

• load_movie_data

• text_to_numbers

• normalize_text

• build_dictionary

What is the purpose of the noise contrastive estimation loss in the CBoW model?

• To initialize word embeddings

• To split the dataset into training and test sets

• To handle sparse categorical output

• To preprocess the movie review data

Which optimizer is used in the training of the CBoW model?

• Adam optimizer

• RMSProp optimizer

• Gradient descent optimizer

• Stochastic gradient descent

What is the next step after training the CBoW model?

• Evaluating the model using a test set

• Building a dictionary of words

• Saving the trained model for future use

• Normalizing the text data

How is the performance of the logistic regression model evaluated?

• By splitting the dataset into training and test sets

• By building a dictionary of words

• By normalizing the text data

• By checking the accuracy of predictions